车联网中基于神经网络的入侵检测方案

车联网的入侵检测（IDS）可用于确认交通事件通知中描述的事件的真实性。当前车联网IDS多采用基于冗余数据的一致性检测方案，为降低IDS对冗余数据的依赖性，提出了一个基于神经网络的入侵检测方案。该方案可描述大量交通事件类型，并综合使用了反向传播（BP）和支持向量机（SVM）2种学习算法。这2种算法分别适用于个人安全驾驶速度快与高效交通系统检测率高的应用。仿真实验和性能分析表明，本方案具有较快的入侵检测速度,且具有较高的检测率和较低的虚警率。

Intrusion detection scheme based on neural network in vehicle network

Vehicle networking intrusion detection solutions (IDS) can be used to confirm the authenticity of the events described in the notice of traffic incidents. The current Vehicle networking IDS frequently use detection scheme based on the consistency of redundant data, to reduce dependence on redundant data, an intrusion detection scheme based on neural network is presented. The program can be described as a lot of traffic event types , and the integrated use of the back-propagation (BP) and support vector machine (SVM) two learning algorithms. The two algorithms respectively applicable to personal safety driving fast and efficient transportation system with high detection applications. Simulation results and performance analysis show that our scheme has a faster speed intrusion detection, and has a high detection rate and low false alarm rate.